Part-of-Speech Tagging and Hidden Markov Models

Part-of-Speech Tagging and Hidden Markov Models #

Part-of-Speech tagging assigns a grammatical category to each word in a sequence. Because many words can play different grammatical roles, a tagger must use surrounding context rather than examine each word independently.

Learning Objectives #

• Identify common English word classes and Penn Treebank tags.
• Explain why POS tagging is a sequence-labelling problem.
• Describe the Markov assumption.
• Distinguish a Markov Chain from a Hidden Markov Model.
• Explain how an HMM represents POS tagging.

Big Picture #

flowchart TD
    A["Word Sequence"] --> B["Use Context"]
    B --> C["Infer Hidden Tags"]
    C --> D["Tagged Sequence"]

    style A fill:#E1F5FE
    style B fill:#C8E6C9
    style C fill:#FFF9C4
    style D fill:#EDE7F6

1. What Is Part-of-Speech Tagging? ☆ #

A part of speech describes the grammatical role played by a word in a sentence. POS tagging assigns one tag to every word in a sequence.

Statistical, ML and Neural Models of POS Tagging

Statistical, ML and Neural Models of POS Tagging #

‘HMM Inference: Forward and Viterbi Algorithms’ covers the portion:

• Forward Algorithm
• Viterbi Algorithm
• HMM inference for POS tagging

The complete Topic also includes:

• Maximum Entropy Markov Models
• Bidirectionality
• Neural-network models for POS tagging

HMM Inference: Forward and Viterbi Algorithms #

Hidden Markov Models create two closely related inference problems:

• Likelihood: How probable is an observed sequence under the model?
• Decoding: Which hidden-state sequence most probably generated the observations?

The Forward Algorithm solves the likelihood problem, while the Viterbi Algorithm solves the decoding problem. Both use dynamic programming and a trellis, but they combine paths differently.